Marginal plots live on the “margins” of a main plot, adding secondary information about the X and Y variables.
Layouts
Marginal plots live in different positions around the main plot. They may appear on one side or on two sides, most often above (x-axis) and to the right (y-axis). You can also add layers, stacking multiple plot types on the same margin for richer context.
Caution
The main plot should always be the star.
Marginal plots are there to provide secondary context, not to steal the spotlight.
Inventory
Marginal plots come in many forms. Histograms, boxplots, density curves, strip plots, and violins are common choices, though almost any plot type can be used. When you add more than one marginal, they can either match (same type) or be mixed (different types) to enrich the story. You can also layer them to add even more context.
Caution
More isn’t always better.
Too many marginals can clutter your figure and make it exhausting to read.
Build your own marginal plots
Some common recipes used in Python and R
Python (Seaborn, jointplot)
import seaborn as snsimport matplotlib.pyplot as plt# Datasettips = sns.load_dataset("tips")# Lines to generate the MAIN + MARGINAL PLOTSg = sns.jointplot( data=tips, x="total_bill", y="tip", kind="scatter", height=6, marginal_ticks=True, joint_kws={"s": 80, "alpha": 0.8})sns.set_theme(style="white")plt.subplots_adjust(top=0.9)# Title & Subtitleplt.suptitle("Relationship between total bill and tip.\n""With marginal histograms showing distributions", fontsize=14, fontweight="bold", y=1)# Avoid this line_ = g.set_axis_labels("Total bill (USD)", "Tip (USD)", labelpad=10)# Optional: some cleaningfor ax in (g.ax_marg_x, g.ax_marg_y): _ = sns.despine(ax=ax, top=True, right=True, left=True, bottom=True) ax.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)plt.show()
R (ggplot2+ggExtra)
library(ggplot2)library(ggExtra)# regular ggplot codep2 <-ggplot(mtcars, aes(x = mpg, y = wt)) +geom_point(color ="darkgreen", size =4, alpha =0.6)+theme_classic()+labs(x ="Miles per gallon (mpg)",y ="Weight (1000 lbs)",title ="Fuel efficiency vs. vehicle weight",subtitle ="With marginal densities" )# Lines to add the MARGINAL PLOTSggMarginal(p2, type ="density", fill ="lightgreen", color ="darkgreen", alpha =0.5)
